Identifying and Assessing Key Weather-Related Parameters and Their Impacts on Traffic Operations Using Simulation

Publication Number: FHWA-HRT-04-131
Date: September 2004

Identifying and Assessing Key Weather-Related Parameters and Their Impacts on Traffic Operations Using Simulation

1. Introduction

Background

Adverse weather conditions can have a dramatic impact on the operations and quality of traffic flow. For example, icy pavement conditions can affect the acceleration and deceleration capabilities of vehicles. Reduced visibility can cause drivers to alter their desired speed,how they change lanes, and how they follow other vehicles. Major weather events can cause drivers to modify their travel patterns, such as taking a different route to a destination, leaving for a destination at a different time than normal, or canceling a trip altogether.

With the advent of ATMS, there is an opportunity to develop traffic management strategies that attempt to minimize the negative weather-related impacts on traffic operations. For instance, a weather event that reduces the average operating speed on an arterial can be mitigated by quickly implementing traffic signal plans that account for the lower speeds while still maintaining progression through a network. However, to develop and implement strategies that minimize the effects of adverse weather conditions, a more complete knowledge of how weather events affect traffic operations and how to assess the weather-related effects for a given scenario is needed.

Currently, the relationship between weather events and traffic operations
is moderately understood, but only at a macroscopic analysis level, such as
the methodologies presented in the Highway Capacity Manual (HCM).
[1](2) Using an HCM-style analysis is, in fact, one way to
model weather impacts to develop weather-responsive traffic management strategies.
However, a more detailed and potentially more accurate method is to use a
microscopic traffic simulation model. A microscopic simulation tool can model
individual vehicles on a roadway network, typically on a second-by-second
basis or less. Simulation models have the benefit of being able to model complex
roadway geometries, traffic control devices, and vehicle configurations that
are beyond the limitations of a macroscopic HCM-style analysis.

However, modeling microscopic driver behavior is difficult under ideal weather
conditions, let alone under adverse weather conditions. Little research has
been conducted on how weather events impact microscopic driver behavior logic,
such as lane changing and vehicle following, both of which are crucial to
the accuracy of a microscopic traffic simulation model. In addition, a vast
number of user-input parameters within simulation models can be altered. Knowing
which key parameters within a microsimulation model should be changed under
various weather conditions would aid greatly in developing weather-responsive
traffic management strategies.

The objectives of this study are to identify how weather events impact traffic
operations, assess the sensitivity of weather-related traffic parameters in
the CORSIM traffic simulation model, and develop guidelines for using the
CORSIM model to account for the impacts of adverse weather conditions on traffic
operations. More specifically, this study is tasked to do the following:

Research the relationship between weather events and traffic operations.

Identify which types of simulation parameters could be affected by weather events.

Conduct a sensitivity analysis on selected CORSIM simulation parameters to identify the key weather-related parameters that most affect traffic operations.

Develop basic guidelines on how weather events can be modeled using CORSIM.

This study does not recommend specific values (e.g., free-flow speed of
70 kilometers/hour (km/h)) to be used for each parameter under various weather
conditions. Rather, it focuses on identifying the general sensitivity of
a parameter to traffic operation MOEs (i.e., average speed). This information
then may be used to develop guidelines on how CORSIM can be used to model
weather events.

This study, which began in September 2002, was conducted on a task order
basis with a total of five tasks. Figure 1 shows a flowchart of the task breakdown and workflow. As shown in
this figure, the tasks were completed in consecutive order, because the
output from one task was required for the next task. This report provides
the results for each of these tasks.

This report represents the final task (Task E in figure 1) for the "Identifying and Assessing Key Weather-Related Parameters and Their Impacts on Traffic Operations Using Simulation" project. This report is separated into the following sections:

Section 1 discusses the objective and approach of the project, including a background discussion on the need for the study.

Section 2 discusses the general relationship between weather events and traffic operations, including a discussion of how a change in weather leads to a change in the quality of traffic flow.

Section 3 discusses the results of a literature search on field studies of the effects of adverse weather on traffic operations parameters.

Conceptually, it is easy to understand that a major weather event, such as a snowstorm, will lead to lower average speeds and higher delays. However, it is important to understand what this relationship is, or in other words, what causes a weather event to degrade traffic operations.

Figure 2. Relationship Between Weather Events and Traffic Operations.

Figure 2 shows the general relationship between weather events and the resulting
impact on traffic operations. This relationship is similar to that shown by
Pisano and Goodwin, with the exception that the definition of "traffic operations"
has been divided into two subparts: traffic parameters (or characteristics)
and quality of traffic flow.(3) Traffic parameters are quantitative
values that typically are used as inputs to a traffic analysis model. These
parameters account for how drivers and their vehicles interact and respond
to the roadway network, including the response to other vehicles, traffic
control devices, roadway geometry, weather, and other environmental conditions.
The quality of traffic flow is the output from a traffic analysis model and
is calculated using MOEs. MOEs measure the overall performance of the transportation
system, which is directly related to how well drivers and their vehicles respond
to the surrounding factors (traffic parameters). Common MOEs include average
speed, average density, average delay per vehicle, and number of stops.

This distinction between the input and output in traffic
operations is important because traffic analysts need to know, for a certain
weather event, which traffic parameters to change and how much to change them
when inputting these parameters into a traffic analysis model. These changes
will produce a new quality of traffic flow reflecting the impacts of the
weather event.

Weather events are any meteorological occurrence that
causes weather conditions to degrade from the "ideal" weather condition. The ideal weather condition is defined as
having the following conditions:

Weather events can change quickly in severity and in coverage area. These
changes over time and space present a challenge in modeling weather events
in a traffic analysis model. The ranges of possible weather events that
are addressed in this study include rain, snow, sleet, hail, flooding, fog,
ice, sun glare, lightning, dust, wind, and extreme temperatures.

Relationship Between
Roadway Environment and Weather Event

Weather events cause a change in the "roadway environment," a term used
by Pisano and Goodwin, meaning a physical change in the roadway or roadway
devices, or a change on the immediate environment surrounding the roadway
(including the driver), and vehicle changes.(3) Each weather
event impacts the roadway environment differently.Table
1 shows the connection between weather events and the roadway environment.
As shown in the table, various weather events, such as fog, dust, rain,
snow, sleet, hail, and sun glare, can reduce driver visibility.

As the roadway environment changes, resulting changes in traffic parameters
will occur. For example, a reduction in driver visibility will logically
cause most drivers to drive more cautiously, to some degree. This changed
driver behavior is reflected in simulation traffic parameters, such as lower
free-flow speeds and more cautious lane changing and car following parameters.
Traffic parameters represent values that a traffic engineer can control
in a simulation model. The ability to modify these parameters in a simulation
model provides the means for simulating the impacts of adverse weather conditions.
The challenge with microscopic simulation models like CORSIM is that they
require many more input traffic parameters than a macroscopic HCM-style
model due to the complex modeling of driver behavior on an individual vehicle
basis.

Before tracing which traffic parameters are impacted by a change in the
roadway environment, it is important to understand the full range of parameters
available in a microscopic simulation model. Table 2 displays a generic
list of possible traffic parameters in a microscopic simulation model.
The parameters are considered generic because they are not specific to any
one model, and the majority of them are included in most simulation models
currently available. However, each model uses slightly different terminology
to define these parameters. Therefore, the parameters listed in table 2
may only be a subset of the actual simulation models parameters. For example,
there are more than 20 parameters in CORSIM that are used to model lane
changing behavior.

Tracing which traffic parameters are likely affected by weather
events (through a change in the roadway environment) was performed based
on a review of table 2, the literature review (section 3), and engineering
judgment. The results of this analysis are presented in section 4 after
the literature review section.

3. Literature Review

Past research on the simulation of traffic operations under adverse weather
conditions can be organized into two main groups: those focusing on the
link between weather events and traffic parameters (i.e., heavy rain reduces
free-flow speeds by 30 percent), and those focusing on the link between
weather events and the quality of traffic flow (i.e., heavy rain increases
delays by 40 percent). This review focuses on the former, because knowing
the impact of weather events on traffic parameters is the key to using microsimulation
to model weather events.

Very little research focusing on the roadway environment impacts shown
in table 1 were found. This lack of information probably is due to the difficulty
in understanding why motorists respond to a weather event (i.e., is a reduction
in free-flow speed really due to a reduction in pavement friction or reduction
in visibility?) The literature review yielded information on the impacts
of weather events on the following traffic parameters: free-flow speed,
startup lost time, saturation headway, and traffic demand.

Free-Flow Speed

A number of studies have shown that adverse weather events reduce the mean
free-flow speed, which is defined as the desired speed of drivers in low
volume conditions and in the absence of traffic control devices.(2)
The amount of reduction in free-flow speed is directly related to the severity
of the weather event. Kyte et al. studied the free-flow speed on a rural
freeway during wet and snow-covered pavement, high wind (greater than 24
km/h), and low visibility conditions (less than 0.28 km).(4)
They found the free-flow speed reduced by approximately:

10 km/h (8 percent) during wet pavement.

16 km/h (13 percent) during snow-covered pavement.

17 km/h (14 percent) during high wind.

18 km/h (15 percent) during low visibility.

35 to 45 km/h (30 to 38 percent) during a combination of snow-covered
pavement, low visibility, and high wind.

May showed that the free-flow speed on freeways was reduced by approximately:(5)

8 percent under light rain or snow.

17 percent under heavy rain.

Up to 40 percent under heavy snow.

Based on a study of two-lane rural highways, Lamm, Choueiri, and Mailaender found that drivers do not adjust their speeds very much under light rain or wet pavement, but they do reduce speeds when visibility becomes obstructed, such as during a heavy rain.(6)

On a sample of freeways in Canada, Ibrahim, and Hall also found that free-flow speed is noticeably decreased during heavy rain and snow; heavy snow (up to 50 km/h reduction) has a much greater effect than heavy rain (up to 10 km/h reduction).(7)

Other studies have shown a reduction in average speed on arterials.(9,10) Average speed, a typical MOE used by traffic engineers, is a different value than free-flow speed; average speed accounts for the effects of signal timing and other effects related to the interaction with other vehicles.

Startup lost time is defined as the additional time consumed by the first few vehicles in a queue at a signalized intersection beyond the saturation headway.(2) This additional time is due to the time to react to the start of the green phase and for the vehicle to accelerate from a stopped position. Under ideal conditions, the HCM recommends using 2.0 seconds for startup lost time.

Maki measured an increase in startup lost time of 50 percent, from 2.0 seconds during normal conditions to 3.0 seconds under adverse weather conditions, which was defined as being a storm with accumulation of 7.6 centimeters (cm) or more of snow, on a signalized expressway in the Minneapolis/St. Paul, MN area.(9)

Perrin, Martin, and Hansen measured a startup lost time increase of approximately 25 percent, from 2.0 to 2.5 seconds, under severe snow-related conditions.(8) However, only a small difference, from 2.0 to 2.1 seconds, was measured during rain-related conditions.

Saturation headway, or discharge headway, is defined as the average headway
between vehicles occurring after the fourth vehicle in a signalized intersection
queue and continuing until the last vehicle in the initial queue clears
the intersection.(2) Saturation headway (expressed in units of
seconds/vehicle (s/veh)) is the inverse of saturation flow rate (veh/s or
veh/h). For example, a 10 percent increase in saturation headway equates
to a 10 percent decrease in saturation flow rate. The HCM recommends an
ideal discharge headway of 1.9 seconds (equates to a saturation flow rate
of 1900 passenger cars/h/lane). This value then is reduced based on adjustments
for lane width, heavy vehicles, grade, adjacent parking, bus blockage, area
type, lane utilization, right and left turns, pedestrians, and bicyclists.

Perrin, Martin, and Hansen measured an average reduction in saturation
flow rate of between 6 and 20 percent, increasing with weather severity
(snow packed on the street surface being the highest severity).(8)

Maki found a saturation flow rate reduction of approximately 10 percent,
from 1800 to 1600 veh/h/lane under adverse weather conditions as defined
above.(9)

Botha and Kruse measured the effect of residual ice and snow on a signalized
arterial in Fairbanks, AK. Saturation flow rates were found to be approximately
20 percent lower than the ideal HCM-recommended conditions.(11)

Maki measured a reduction in traffic volumes of 15 to 30 percent during
adverse weather conditions when compared to ideal weather conditions.(9)
The reduction in traffic volumes was attributed to various reasons, including
shifting work arrivals and departures, and avoidance of discretionary trips.
Traffic demand changes depend strongly on the severity of the weather conditions
and the driver's comfort in adverse weather conditions. For example, drivers
in Chicago, IL will react differently to a snowstorm than will drivers in
Miami, FL.

4. Identifying Simulation Parameters Affected by Weather Events

The literature review documented a number of traffic parameters that were
found to be impacted by weather events. However, there are numerous other
microsimulation parameters that have not been measured empirically to behave
differently during adverse weather. It is important to identify these parameters
and include them in the sensitivity study.

Tables 3 through 7 show the traffic simulation parameters that likely are
impacted by weather events (through a change in the roadway environment).
The selection of these parameters was based on the range of simulation parameters
identified in table 1, the literature review, and the use of engineering judgment
based on the concept that driver behavior becomes more conservative during
adverse weather conditions. Unfortunately, there is currently no empirical
research supporting this concept. Therefore, the table only lists the range
of potential, not proven, simulation parameters that may be used to model
adverse weather conditions in a simulation model. These simulation parameters
may be used as a guide for traffic analysts when considering which parameters
to adjust when modeling adverse weather.

The remainder of this section discusses how parameters in each major category
(road geometry, traffic control and management, vehicle performance, traffic
demand, and driver behavior) may be impacted by weather events.

Table 3 displays road geometry parameters likely impacted by weather events
though a change in the roadway environment. If available in a simulation model,
the pavement condition parameter should be modified during a weather event,
causing a reduction in pavement friction. The traffic analyst should be aware,
however, how the pavement condition parameter affects other parameters. For
example, changing the pavement condition parameter in FRESIM (the freeway
model within CORSIM) causes an automatic reduction in free-flow speed for
a link in a horizontal curve. Also, a weather event causing a lane or shoulder
blockage would alter the number and width of available lanes, length of tapers
associated with lane adds and drops, and shoulder width.

Table 3. Road
Geometry Traffic Parameters Impactedby Weather Events.

Generic Traffic Simulation
Parameter

Weather Events

Fog, Dust, Rain, Snow, Sleet, Hail, Sun Glare

Ice, Rain, Snow, Sleet, Hail, Flooding

Wind, Ice, Rain, Snow, Sleet, Hail, Flooding

Ice, Rain, Snow, Sleet, Hail, Flooding

Extreme Temperatures, Lightning, Wind

Roadway Environment Impact

Reduced Visibility

Reduced Pavement Friction

Reduced Vehicle Maneuverability/Stability

Blocked Lanes/ Covered Signs and Pavement Markings

Failed Traffic Control Devices and Communications

Pavement condition

X

Number of lanes

X

Lane width

X

Lane taper length

X

Shoulder width

X

Traffic Control and Management Parameters

Table 4 displays traffic control and management parameters likely impacted
by weather events though a change in the roadway environment. A reduction
in visibility would make it difficult for drivers to see traffic signals or
signs. Thus, the parameters related to sight or reaction distance to the traffic
signals and signs would need to be altered. Also, a weather event that caused
a sign blockage would require altering the parameters related to the visibility
of, and compliance with, traffic signs. Finally, a weather event causing a
power failure and loss of communications between traffic devices or to a traffic
management center would require altering the traffic signal settings (i.e.,
change to emergency flash operation), or removing the functionality of detector
devices, including those used at traffic signals, ramp meters, or systemwide
surveillance.

Vehicle Performance Parameters

Table 5 displays vehicle performance parameters likely impacted by weather
events though a change in the roadway environment. A reduction in pavement
friction could affect the acceleration and deceleration capabilities of vehicles.
These parameters relate to the performance of the vehicle only, and not necessarily
the behavior of the drivers. The acceleration and deceleration capability
of vehicles typically are used in the car following and lane changing logic
of a simulation model; therefore, changing these parameters likely will alter
the way vehicles follow each other and change lanes in a model.

Table 6 displays traffic demand parameters likely impacted by weather events
through a change in the roadway environment. Any weather event causing one
or more major roadway environment impacts could cause a change in vehicle
demand and route choice. For example, a major snowstorm over an entire city
could cause vehicle demand to be reduced on all links, whereas an isolated
storm affecting only a small number of roads could result in no change in
overall traffic demand but different route choices, because drivers would
avoid the impacted roads. Many simulation models allow the input of traffic
demands as origin-destination pairs with a traffic assignment procedure (which
determines the preferred route for motorists in traveling between their origin
and destination) built into the model. For these models, changing the appropriate
parameters to reflect the conditions of the snowstorm on the isolated roads
would allow the traffic assignment algorithm to predict automatically the
change in route choice associated with the snowstorm.

Table 7 displays driver behavior parameters likely impacted by weather events
though a change in the roadway environment. Many driver behavior parameters
are impacted by weather events causing visibility, pavement friction, or vehicle
maneuverability reductions. Car following and lane changing behavior likely
will be more cautious during weather events, with the degree of caution dependent
on the severity of the weather event. Free-flow speed, startup lost time,
and discharge headway all have been documented to degrade during weather events.
In addition, intersection-related parameters such as gap acceptance, turning
speed, and responses to the yellow interval likely are impacted by weather
events.

[1] HCM methodologies do not specifically address the impacts of weather events on highway capacity and quality of service: however, the parameters in the HCM could be user-adjusted to reflect the impacts of weather events.